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Seismic Multi-attribute Classification for Salt Boundary Detection:
A Comparison13 June, 2017
Haibin Di* and Ghassan AlRegib Center for Energy and Geo Processing,
Georgia Institute of Technology, Atlanta, GA
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Outline Introduction Proposed workflow Result analysis Conclusion
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Structural Interpretation Salt bodies are important geologic structures for subsurface hydrocarbon exploration
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Salt dome
Salt
Large-scale Seismic Data Acquisition
Manual Interpretation Time consuming Labor intensive
Motive for automated interpretation
Migrated Seismic Volume Seismic Interpretation [1-2]
[1] http://csegrecorder.com/articles/view/advances-in-true-volume-interpretation-of-structure-and-stratigraphy-in-3d[2] http://www.dgi.com/earthvision/evnews/evnews.html
1 km2 seismic survey for one hour
Seismic surveys: PetaBytes of data
Generate 1TB migrated data
Years for manual interpretation
Up to 160 shots/1 km2
20,000 traces per shot, every 10s
500 samples per trace per second
Generate 600GB data
ComputationalSeismic Interpretation
Challenges
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Motivation Manual interpretation is labor-intensive and time-
consuming for large seismic datasets; Computational seismic interpretation (e.g., object
detection, facies analysis) is a relatively recent research focus
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ComputationalSeismic Interpretation
Objective To verify the application of various machine learning algorithms to
seismic feature detection: Provide a fair comparison between different ML Use salt boundary detection as an example
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Salt domeSalt dome
Motivation Various approaches have been developed from other
disciplines, includinga. Edge detectionb. Texture analysisc. Seismic Saliencyd. Machine learning-based classification
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Sobel filter Salt likelihood
Common ML approaches Logistic regression
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Decision tree
Logistic function to link features and labels
Flowchart-like structure: non-leaf for features, and leaves for labels
Common ML approaches Random forest
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Support vector machine
An ensemble of decision trees Hyperplane boundary in feature domain
Common ML approaches Artificial neural network
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K-mean cluster analysis
Clustering of observations into separate groups
Complicated process mimicking the brain activities
Proposed workflow
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Load seismic amplitude
Attribute 1
Train models: SVM, ANN, k-means et al.
Pick training samples
Select seismic attributes
Attribute 2
.
Attribute N
Volumetric processing
Proposed workflow
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Step A: Attribute selection (12)
GLCM standard deviation
RMS amplitude
GLCM dissimilarity
GLCM variance
GLCM contrast
GLCM entropy
GoT
GLCM ASM
GLCM homogeneity
Saliency
GLCM energy
Canny edge
Proposed workflow
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Attribute vs.
salt boundary
Attribute Measurement Salt boundary Non-boundary
RMS amplitude Reflection intensity High Low
GLCM angular second moment
Spatial arrangement of seismic amplitude in
various statistical approaches
Low High
GLCM contrast High Low
GLCM dissimilarity High Low
GLCM energy Low High
GLCM entropy High Low
GLCM homogeneity Low High
GLCM standard deviation
High Low
GLCM variance High Low
Gradient of textureVariation of seismic
textureHigh Low
SaliencyAttention from an
interpreterHigh Low
Canny edge detection
Lateral similarity of waveform/amplitude
Low High
Human visual system (HVS) is sensitive to
Structure Motion Surrounding information
Attention models mimic the behavior of human subjects looking at an image or video
A new attribute: Seismic SaliencyImage
Formation
Light and Exposure Control
Visual Processing
[1] https://www.studyblue.com/notes/note/n/visual-process-and-perception/deck/14629318[2] http://ivrlwww.epfl.ch/supplementary_material/RK_CVPR09/
Human Visual System [1]
Saliency Detection [2]
Detection
Attention Models in Seismic Interpretation
Human visual system
Attention models based on
Interpretation
Detection and delineation of salt domes
Detection and delineation of faults
Structural
[1] https://agilescientific.com/blog/2013/8/6/your-next-employment-contract.html
Attention Models based on HVS
We recently proposed a FFT-based saliency detection method, which compared toother detection algorithms
Effectively captures temporal and spatial saliency Have better computational efficiency Require few parameters selection
Saliency Detection
Zhiling Long and Ghassan AlRegib, Saliency detection for videos using 3D FFT local spectra, Proc. SPIE, vol. 9394, pp. 93941G93941G6, 2015.
Ft
FsF
Geometric Decomposition
Temporal Energy Distribution
Spatial Energy Distribution
A Video Frame
Seismic Saliency
M. A. Shafiq, Z. Long, T. Alshawi, and G. AlRegib, Saliency detection for seismic applications using multi-dimensional spectral projections and directional comparisons, IEEE International Conference on Image Processing (ICIP), Beijing, China, Sep. 17-20, 2017.
Saliency Detection
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Crossline Section Saliency Map
Saliency Detection
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Time Section Saliency Map
Proposed workflow
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Step B: Training sample picking (879)
1.5 s
1.6 s
1.7 s
XL 700 XL 800 XL 900 XL 1000
1.4 s
-4000 +4000
Manual picking on one single vertical section (crossline 415), including 197 pickings on the salt-dome boundary (denoted as cyan dots); 682 pickings on the surrounding non-boundary features (denoted as magenta dots).
Proposed method
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Step C: Testing
(a) Logistic regression (863)(b) Decision tree (875)(c) Random forest (876)(d) Support vector machine (864)(e) Artificial neural network (843)(f) K-mean clustering (857)The re-clustering of the 879pickings Good accuracy Minor mis-clustering where the
seismic signals are similar tosalt boundaries
Proposed method
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Step D: Volumetric processing
At each sample in a volume, the twelve attributes are retrieved, based on which the trained modelgives its prediction. Such processing repeats at ALL samples.
Trained model
Probability: 0
Probability: 1
Probability: 0
Result analysis
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Salt probability
Salt-boundary probability from(a) Logistic regression(b) Decision tree(c) Random forest(d) Support vector machine(e) Artificial neural network(f) K-mean clustering
Original seismic amplitude
Result analysis
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3D view
Good match is observed byoverlaying the probability volumeon the original seismic amplitudefrom(a) Logistic regression(b) Decision tree(c) Random forest(d) Support vector machine(e) Artificial neural network(f) K-mean clustering
Result analysis
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Salt surface extractionSeeded tracking on the salt probability volume
3 seeds used in this work as black dots Salt surface
Result analysis
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Salt surface
Salt surface is generated by seed-tracking on the probabilityvolumes from(a) Logistic regression(b) Decision tree(c) Random forest(d) Support vector machine(e) Artificial neural network(f) K-mean clustering
The clipping of salt surfaces to fourrandomly-selected vertical sectionsRed: Logistic regressionMagenta: Decision treeBlack: Random forestYellow: Support vector machineGreen: Artificial neural networkCyan: K-means clustering
Result analysis
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2D view
Conclusion Six commonly-used machine learning approaches are compared for
multi-attribute salt-boundary detection from 3D seismic data with the same configurations,
12 seismic attributes 879 training samples
Similar results are observed, indicating its less sensitivity to ML algorithms
Among the six ML algorithms, k-means is least effective Accuracy is further improved with more training samples and more
complicated ML algorithms (such as CNN)
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Thank You
For more information about the center, please visit:
http://cegp.ece.gatech.edu/
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http://cegp.ece.gatech.edu/
Backup
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Saliency Detection
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M. A. Shafiq, T. Alshawi, Z. Long, and G. AlRegib, The role of visual saliency in the automationof seismic interpretation, accepted in Geophysical Prospecting, March, 2017.
M. A. Shafiq, T. Alshawi, Z. Long, and G. AlRegib, SalSi: A New Seismic Attribute For Salt Dome Detection, IEEE ICASSP, Shanghai, China, Mar. 20-25, 2016.
Seismic Saliency
Seismic Volume Saliency Map
M. A. Shafiq, T. Alshawi, Z. Long, and G. AlRegib, The role of visual saliency in the automationof seismic interpretation, accepted in Geophysical Prospecting, March, 2017.
M. A. Shafiq, T. Alshawi, Z. Long, and G. AlRegib, SalSi: A New Seismic Attribute For Salt Dome Detection, IEEE ICASSP, Shanghai, China, Mar. 20-25, 2016.
Saliency in Interpretation
Saliency Detection Thresholding
Post Processing
V S B
Salt Dome Highlighting
SALIENCY DETECTION